Carbon–Energy Synergistic Optimisation of Park‐Level Integrated Energy Systems: A Multi‐Objective Approach Incorporating Carbon Intensity Incentives

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🔬研究者:The carbon–energy hub model provides a novel formalism for capturing carbon–energy coupling in multi-energy systems, useful for further optimization research.

🏢実務担当者:Park or industrial facility operators can apply the proposed scheduling method to reduce carbon emissions cost-effectively by leveraging carbon intensity incentives.

🏛政策担当者:The results support the design of carbon intensity-based incentive mechanisms for district energy systems, complementing carbon pricing schemes.

With the development of low‐carbon/zero‐carbon parks and carbon trading markets, park‐level integrated energy systems (PIESs) require scheduling methods that can coordinate economic performance and carbon reduction. However, existing studies usually rely on energy price incentives alone or introduce carbon intensity (CI) incentives as exogenous signals, making it difficult to capture carbon–energy interactions during PIES scheduling. Therefore, this paper develops a carbon–energy synergistic multiobjective optimal scheduling method for PIESs. First, a carbon–energy synergistic hub model is proposed to characterise the coupling relationship between carbon and energy within PIESs. The model captures the equilibrium between energy flow and carbon flow and provides CI signals that vary with energy flow changes. On this basis, a multiobjective scheduling model is established to minimise both operating cost and carbon emissions. The model integrates the dual guidance of energy price and CI to regulate multi‐energy conversion devices, energy storage, and flexible multi‐energy loads. A typical‐day case study is conducted using source/load power profiles, time‐varying CI of purchased energy, and energy price parameters, all of which are forecast from historical operating data. The results show that the proposed method provides Pareto‐optimal solutions that better balance economic cost and carbon reduction. Compared with the current energy hub‐based and carbon energy decomposition‐based scheduling methods, the proposed method achieves 7.07% and 4.36% lower minimum carbon emissions, respectively, with only a slight increase in operating cost. These findings suggest that PIES scheduling should place greater emphasis on CI incentives rather than relying only on energy price incentives, while also accounting for the impact of carbon–energy interactions on multiobjective scheduling.

• semanticscholar https://doi.org/10.1049/rpg2.70290first seen 2026-06-19 05:18:44